1. Field of the Invention
The present invention relates to data analysis. More particularly, the invention is a computerized method and software for data analysis using a distribution of independent variable samples graphed in comparison to dependent variable data to identify a correlation between the independent and dependent variables.
2. Description of the Related Art
Industrial and manufacturing processes often employ a complex series of manufacturing steps, applied to one or more raw materials, to produce a product. A business goal is, generally, to maximize the process productivity by eliminating unscheduled downtime of the process, while maintaining a quality standard of the product. Machine processes are typically instrumented, employing a variety of sensors in communication with a computer system to measure and record various parameters relating to raw materials, the process itself, and the product. The data collected is useful to monitor equipment operation for consistency, and to track quality of both raw material and finished product. Additionally, faults that lead to costly production line downtime can be recorded.
In the example of a paper manufacturing process, pulp enters a process and, ultimately, is formed onto paper in the form of a continuous web roll. As the pulp is converted into paper, the paper is transported through the process machinery across rollers and through various pressure nips, according to various phases of the process. It can be recognized that a break in the paper web causes the processing machinery to be shut down while damage waste paper is removed, a remaining good portion of the web re-fed through the machinery, and rejoined to a take-up roll. Thus, it is desirable to determine the cause of any such breaks and to identify a solution. However, given the number of factors that each may play a role; such as the speed of the paper web through processing machinery, pressure applied at various pressure nips in the process, temperatures at various phases of the process, and the tension applied to the paper web, as well as various properties of the pulp used, the solution may not be readily apparent. Additionally, a problem and solution identified for a given grade of pulp may differ substantially from problems encountered with a different pulp.
Various methods have been employed to analyze data gathered relating to an industrial process for the purpose of identifying problem causation. Typically, known instrumentation and data recording systems gather a large amount of data, and then make the data available to commercial computer spreadsheet programs for treatment by standalone statistical analysis applications.
Standalone statistical analysis applications often require that a user manually “cleans” the data, to ensure that only good data is included in the analysis. Data that is out-of-range, or is indicative of (or resulting from) a sampling error or failure, or that is otherwise considered to be bad data must be eliminated. Additionally, within the remaining set of good data, there may be data that is not of interest, such as good data that does not fall within certain criteria of interest for a particular analysis. Further, limitations such as a minimum desired number of observations within a time interval must be considered.
The statistical techniques used are based on parametric statistics, requiring certain assumptions to be made initially to produce usable results. Such assumptions are often ignored, leading to compromised results. Most tools using a parametric method employ regression-based analysis, often leading to a problem of causation versus correlation.
Consequently, there is a need for an automated process for mining data accumulated from a computerized data historian that monitors a manufacturing process, and analyzes the data to determine factors in the manufacturing process that need to be altered in order to improve the efficiency of the manufacturing process. Thus, a computerized method and software for data analysis solving the aforementioned problems is desired.